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A secure and cloud-based patient management system using attribute-based encryption algorithm

10.11591/ijece.v15i2.pp2445-2454
Senthilkumar Kalarani , Mahalingam Shobana , Edamakanti Uma Shankari , Bolly Joshi Praveena , Subramaniam Shanthi , Rathinasabapathy Ramadevi , Rajendar Sandiri
Using attribute-based encryption (ABE), cloud-based patient management systems may be made more secure and efficient. The goal is to provide a scalable encryption infrastructure with dynamic attribute handling and context-aware access control for safe data access. Encryption procedures should directly comply with regulatory criteria to secure healthcare data and ensure data privacy and integrity. Secure attribute issuance and revocation are achieved using advanced key management and real-time auditing and monitoring to identify and react to unauthorised access. To help healthcare providers handle data, user-centric security measures including extensive training and adaptive security procedures are used. The encryption system is implemented and maintained using cost-effective cloud and open-source methodologies to ensure seamless integration and operational effectiveness in healthcare contexts. First, secure patient management system dataset results reveal ABE algorithm encryption. The encrypted values are 8F5D6A..., 7C4A3B..., 6E3B2C..., 9D8A7B..., 5E4D3C.... in the second instance, derived from role-based access control of ABE. The patients are 25-60 years old, have medical codes 101-105, 201-205, and 301-305. For roles from different fields, attribute code is 401-406, level code is 501-505.
Volume: 15
Issue: 2
Page: 2445-2454
Publish at: 2025-04-01

Teacher technology usage, a catalyst for principal digital leadership practice

10.11591/ijere.v14i2.31914
Rui Zhu , Bity Salwana Alias , Mohd Izham Mohd Hamzah , Jamalullail Abdul Wahab
In the digital age, university principals and teachers share the same responsibility for improving educational digital transformation. However, their performance remains disadvantaged. This study aims to investigate the relationship between principal digital leadership and teacher technology usage, and explores how teacher technology usage contributes to principal digital leadership. The study employed a quantitative method, collecting data from 500 teachers across 25 universities in Jilin Province, China. Pearson correlation analysis examined the association between principal digital leadership and teacher technology usage, while multiple regression explored how teacher technology usage contributes to principal digital leadership practices. The results revealed a positive relationship between principal digital leadership and teacher technology usage. All four dimensions of teacher technology usage contribute to digital leadership practices, with the professional development dimension showing the greatest impact. Overall, these findings prove that teacher technology usage indeed catalyzes principal digital leadership practices, which contributes to understanding of the interaction in leadership and provides a new perspective for enhancing principal digital leadership. It implies that principals and teachers can grow together in their interactions. Therefore, strengthening teachers’ professional development can contribute to principal digital leadership practice.
Volume: 14
Issue: 2
Page: 1227-1234
Publish at: 2025-04-01

Two-scale decomposition and deep learning fusion for visible and infrared images

10.11591/ijece.v15i2.pp1593-1601
Ruhan Bevi Azad , Hari Unnikrishnan , Lokesh Gopinath
The paper focuses on the fusion of visible and infrared images to generate composite images that preserve both the thermal radiation information from the infrared spectrum and the detailed texture from the visible spectrum. The proposed approach combines traditional methods, such as two-scale decomposition, with deep learning techniques, specifically employing an autoencoder architecture. The source images are subjected to two-scale decomposition, which extracts high-frequency detail and low-frequency base information. Additionally, an algorithmic unravelling technique establishes a logical connection between deep neural networks and traditional signal processing algorithms. The model consists of two encoders for decomposition and a decoder after the unravelling operation. During testing, a fusion layer merges the decomposed feature maps, and the decoder generates the fused image. Evaluation metrics including entropy, average gradient, spatial frequency and standard deviation are employed to subjectively assess fusion quality. The proposed approach demonstrates promise for effectively combining visible and infrared imagery for various applications.
Volume: 15
Issue: 2
Page: 1593-1601
Publish at: 2025-04-01

Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques

10.11591/ijai.v14.i2.pp1232-1241
Lahcen Idouglid , Said Tkatek , Khalid Elfayq
The rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings.
Volume: 14
Issue: 2
Page: 1232-1241
Publish at: 2025-04-01

An improved key scheduling for advanced encryption standard with expanded round constants and non-linear property of cubic polynomials

10.11591/ijece.v15i2.pp2455-2467
Muthu Meenakshi Ganesan , Sabeen Selvaraj
The advanced encryption standard (AES) offers strong symmetric key encryption, ensuring data security in cloud computing environments during transmission and storage. However, its key scheduling algorithm is known to have flaws, including vulnerabilities to related-key attacks, inadequate nonlinearity, less complicated key expansion, and possible side-channel attack susceptibilities. This study aims to strengthen the independence among round keys generated by the key expansion process of AES—that is, the value of one round key does not reveal anything about the value of another round key—by improving the key scheduling process. Data sets of random, low, and high-density initial secret keys were used to evaluate the strength of the improved key scheduling algorithm through the National Institute of Standards and Technology (NIST) frequency test, the avalanche effect, and the Hamming distance between two consecutive round keys. A related-key analysis was performed to assess the robustness of the proposed key scheduling algorithm, revealing improved resistance to key-related cryptanalysis.
Volume: 15
Issue: 2
Page: 2455-2467
Publish at: 2025-04-01

Explainable zero-shot learning and transfer learning for real time Indian healthcare

10.11591/ijict.v14i1.pp91-101
Swati Saigaonkar , Vaibhav Narawade
Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Volume: 14
Issue: 1
Page: 91-101
Publish at: 2025-04-01

Intrusion detection based on generative adversarial network with random forest for cloud networks

10.11591/ijece.v15i2.pp2491-2498
Gnanam Jeba Rosline , Pushpa Rani
The development of cloud computing enables individuals and organizations to access a wide range of online programs and services. Because of its nature, numerous users can access and distribute cloud infrastructure. In cloud computing several security threats change the data and operations. A network's ability to detect malicious activity and possible threats is greatly aided by intrusion detection. To solve these issues, intrusion detection based on generative adversarial network with random forest (GAN-RF) for cloud networks is introduced. The function of the generative adversarial networks (GANs) based network abnormality recognition system is evaluated. It uses the CICIDS2018 dataset to detect intrusion. GAN is utilized to improve network anomaly detection in conjunction with an ensemble random forest (RF) classifier. The GAN-RF model achieved 95.01% of accuracy for intrusion detection and obtain better recall and F1-score. Extensive assessments and valuations illustrate the efficiency of the GAN-RF approach in accurately identifying network issues.
Volume: 15
Issue: 2
Page: 2491-2498
Publish at: 2025-04-01

Opportunities and challenges while conducting field trips to the museum: a narrative review

10.11591/ijere.v14i2.29297
Chatterjee Sahani , G. S. Prakasha
The museum visit field trip engages and motivates the children in various activities. Field trips to the museum provide the students with a constructivist and experiential learning environment as they construct knowledge through observing the artifacts. The present study describes the possible opportunities and challenges for school children while conducting field trips to the museum. The study employed a narrative review technique to address the research question raised. The study selected the literature reviews from 2012-2023, including studies on field trips to the museum for the academic engagement of school children. The data includes 50 peer-reviewed journal articles categorized into five categories: students’ overall development, experiential learning opportunities, the museum as a resource, the role of teachers, the school, and museum authority. Results revealed that the museum is a resource for learning and is perfect for improving students’ cognitive and affective development towards the various school subjects and helping them enhance their participatory learning opportunities. However, teachers’ knowledge, infrastructure, parental consent, and legitimization with the school authority are some challenges in conducting museum visit field trips. Future research may focus on conducting empirical studies, which include school-museum collaboration, to enhance the horizon of school and community knowledge.
Volume: 14
Issue: 2
Page: 871-878
Publish at: 2025-04-01

Connected caregiving: investigating mothers in the era of digital access

10.11591/ijict.v14i1.pp347-354
Anissa Saidi , Wong Yee Von , Tirzah Zubeidah Zachariah@ Omar , Lim Seong Pek , Rita Wong Mee Mee , Khoo Kim Leng
Mothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion.
Volume: 14
Issue: 1
Page: 347-354
Publish at: 2025-04-01

Android-based smart digital marketplace application on agricultural commodities using a new variant recommendation system

10.11591/ijece.v15i2.pp1968-1977
Subiyanto Subiyanto , Sucihatiningsih Dian Wisika Prajanti , Nur Azis Salim , Setya Budi Arif Prabowo , Deyndrawan Sutrisno , Andika Anantyo , Dewi Anggriani
In the marketing of agricultural products, addressing the challenges associated with extensive distribution chains is essential, as these directly affect sellers. Additionally, the vast array of available product options often overwhelms customers, complicating their efforts to identify and purchase items that align with their preferences. This work aims to develop a smart e-commerce application for agribusiness, specifically designed for agricultural products on the Android platform. The application integrates a recommendation system that utilizes geolocation-aware neural graph collaborative filtering (GA-NGCF), which facilitates product marketing for farmers and streamlines the product search and selection process for users based on personalized preferences. The development process encompassed various stages, from planning to rigorous testing. The application’s recommendation system, which implements GA-NGCF, operates based on three primary elements: the creation of a geolocation graph of user-item data, the integration of information between neighboring nodes, and the prediction of user preferences. The resulting smart agribusiness e-commerce application, enhanced by GA-NGCF, demonstrated marked improvements in recommendation accuracy and overall application performance during testing. Empirical results indicated substantial enhancements in recommendation metrics, with GA-NGCF achieving a recall of 0.34, a precision of 0.36, and normalized discounted cumulative gain of 0.37, thereby outperforming existing models.
Volume: 15
Issue: 2
Page: 1968-1977
Publish at: 2025-04-01

Advancing network security: a comparative research of machine learning techniques for intrusion detection

10.11591/ijece.v15i2.pp2271-2281
Shynggys Rysbekov , Abylay Aitbanov , Zukhra Abdiakhmetova , Amandyk Kartbayev
In the current digital era, the advancement of network-based technologies has brought a surge in security vulnerabilities, necessitating complex and dynamic defense mechanisms. This paper explores the integration of machine learning techniques within intrusion detection systems (IDS) to tackle the intricacies of modern network threats. A detailed comparative analysis of various algorithms, including k-nearest neighbors (KNN), logistic regression, and perceptron neural networks, is conducted to evaluate their efficiency in detecting and classifying different types of network intrusions such as denial of service (DoS), probe, user to root (U2R), and remote to local (R2L). Utilizing the national software laboratory knowledge discovery and data mining (NSL-KDD) dataset, a standard in the field, the study examines the algorithms’ ability to identify complex patterns and anomalies indicative of security breaches. Principal component analysis is utilized to streamline the dataset into 20 principal components for data processing efficiency. Results indicate that the neural network model is particularly effective, demonstrating exceptional performance metrics across accuracy, precision, and recall in both training and testing phases, affirming its reliability and utility in IDS. The potential for hybrid models combining different machine learning (ML) strategies is also discussed, highlighting a path towards more robust and adaptable IDS solutions.
Volume: 15
Issue: 2
Page: 2271-2281
Publish at: 2025-04-01

Video forgery: An extensive analysis of inter-and intra-frame manipulation alongside state-of-the-art comparisons

10.11591/ijai.v14.i2.pp1471-1483
Sumaiya Shaikh , Sathish Kumar Kannaiah
The widespread accessibility of inexpensive mobile phones, digital cameras, camcorders, and security closed-circuit television (CCTV) cameras has resulted in the integration of filmmaking into our everyday existence. YouTube, Facebook, Instagram, and Snapchat are a few of the video-sharing and editing applications that facilitate the process of uploading and editing videos. Additional instances include Adobe Photoshop, Windows Movie Maker, and Video Editor. Although editing has its advantages, there is a potential risk of counterfeiting. This occurs when films are edited with the intention of misleading viewers or manipulating their perspectives, which can be particularly troublesome in judicial procedures where recordings are submitted as evidence. The issue has been exacerbated by the emergence of deep learning methods, such as deepfake videos that effectively manipulate facial characteristics. Consequently, individuals have become less reliant on visual evidence. These issues emphasise the pressing necessity for the creation of dependable methods to determine the authenticity of films and identify cases of fraud. Contemporary methods can depend on assessing modified frames or utilising distortions generated during video codec compression or double compression. Since 2016, multiple studies have been undertaken to investigate techniques, strategies, and applications to tackle this problem. The objective of this survey study is to provide a comprehensive analysis of these algorithms, highlighting their advantages and disadvantages in detecting different forms of video forgeries.
Volume: 14
Issue: 2
Page: 1471-1483
Publish at: 2025-04-01

Stress detection through wearable sensors: a convolutional neural network-based approach using heart rate and step data

10.11591/ijece.v15i2.pp1880-1888
Rifki Wijaya , Gamma Kosala
With the current technological advancements, particularly in sensing technologies, monitoring various health aspects, including heart rate, has become feasible. The problem addressed in this study is the need for effective stress detection methods to mitigate the significant consequences of high-intensity or long-term stress, which impacts safety and disrupts normal routines. We propose a stress detection system developed based on the convolutional neural network (CNN) method to address this. The study involves university students aged 20–22, focusing on mental stress. The dataset encompasses parameters such as heart rate, footsteps, and resting heart rate recorded through a smartwatch with 149,797-row data. Our results indicate that the CNN model achieves an 84.5% accuracy, 80.9% precision, 79.8% recall, and an 80.4% F1-score, confirming its efficacy in stress classification. The confusion matrix further validates the model’s accuracy, particularly for classes 1 and 2. This research contributes significantly to the development of an effective and practical stress detection method, holding promise for enhancing well-being and preventing stress-related health issues.
Volume: 15
Issue: 2
Page: 1880-1888
Publish at: 2025-04-01

An adaptive audio wave steganography using simulated annealing algorithm

10.11591/ijece.v15i2.pp2237-2253
Atef Ahmed Obeidat , Mohmmed Jazi Bawaneh , Sawsan Yousef Abu Shqair , Hamdi A. Al-Omari , Emad Fawzi Al-shalabi
The science of information security has increased in importance to encounter the espionage and information theft. This research proposes a new steganography framework that utilizes simulated annealing (SA) as an artificial intelligence algorithm to support the process of hiding a binary secret message file within an audio wave file. The best path for embedding the secret data inside the audio file is determined through SA that searches for the preferred path according to the content of the host audio file and secret message to be hidden. The least significant bit (LSB) technique was employed to hide message bytes, in which each audio-chosen byte will hold one bit from a secret message byte. The hiding process constructs the stego audio file and extraction key that will be required in an extraction process. The authorized user requires an extraction key and a decryption key to retrieve the hidden message. On the other hand, the attacker requires knowledge of the aforementioned keys and working algorithms that were employed in the hidden process. Robustness against data extraction, detection, imperceptibility (phonological hearing), security, peak signal to noise ratio (PSNR), mean square error (MSE) and capacity as security performance measures were used to evaluate the system. The maximum size of the data to be hidden may reach 12.5% of the data size of the host audio file, in which the average value of MSE and PSNR are (0.0041, 74.73), respectively.
Volume: 15
Issue: 2
Page: 2237-2253
Publish at: 2025-04-01

Enhancing engineering education through virtual reality: a systematic study on immersive engineering education practices

10.11591/ijece.v15i2.pp1889-1899
Tarek Riaji , Sanae El Hassani , Fatima Ezzahrae M'hamdi Alaoui
This article explores the integration of virtual reality (VR) and associated technologies in engineering education, focusing on the pedagogical approaches adopted in this integration, which we refer to as immersive engineering education. This study considers the application possibilities and the transformative impact of VR on engineering education. The article addresses the critical collection and analysis of VR applications in engineering education. It covers main VR-related papers published from 2015 to February 2024 and indexed in Scopus, Web of Sciences, or both, and discussing design, development challenges, and collaborative tools. Empirical evidence showcases improved engagement, motivation, and learning outcomes. The findings offer modern insights for educators and researchers on leveraging VR for impactful learning experiences, while also noting the need for further research in this evolving field.
Volume: 15
Issue: 2
Page: 1889-1899
Publish at: 2025-04-01
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